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Given that the amount of work to be done remains the same, new tools that increase the efficiency of workers will reduce the numbers of workers needed. Thus, ev
by lb4r 4y ago
Given that the amount of work to be done remains the same, new tools that increase the efficiency of workers will reduce the numbers of workers needed. Thus, even without machine learning, new tools make your position worth less and less; if nine people can do tomorrow what ten people can today, that is effectively a threat to your job. What keeps your job safe is a seemingly ever-increasing amount of work to be done where old tools can't be applied for increased efficiency (which is why most software engineers will have to keep their skill set constantly up-to-date). For how long will this be the case, though?
- paxys 4y agoEvery advancement in software engineering productivity throughout history has done the exact opposite. It increases the overall problem space for the profession and you need more and more programmers to meet the demand.
- lb4r 4y agoYes, I 100% agree. I was sort of nitpicking about what kept your job safe since you seemed to imply that it was due to the shortcomings of Deepmind and ChatGPT. I argued that it is due to keeping your skill set updated, and, as you put it more elegantly than I did, the increased overall problem space. I do, however, think there will be an inflection point in the coming decades, where the tools become more generalized and better at dealing with new problems. I might also add that the reason for this belief is simply that a lot of work is being put into making these type of generalized tools; but unlike Kurzweil, I don't quite believe it will lead to the Singularity. :)